Orchestrate LLMs, RAG pipelines, and agent workflows for enterprise AI deployments.
npx skills add https://github.com/AndyAnh174/wellness --skill ai-engineer-andyanh174
Please help me install this Agent Skill. Skill: ai-engineer Source: https://github.com/AndyAnh174/wellness/tree/main/.agent/skills/ai-engineer Command: npx skills add https://github.com/AndyAnh174/wellness --skill ai-engineer-andyanh174
Building production-grade AI applications requires robust architectures, scalable pipelines, and reliable agent orchestration. This Skill provides a blueprint for integrating LLMs, RAG systems, and multimodal capabilities into end-to-end systems that can operate in real-world, enterprise contexts.
Configure a production-grade LLM app by selecting a model, wiring a retrieval pipeline, and exposing a REST API.
High-intent search queries and answers about installing and using this skill.
Production-grade LLM apps require orchestrating LLMs, RAG pipelines, and agent workflows. This blueprint integrates model selection, retrieval, memory, routing, and observability with safety considerations to automate complex workflows in enterprise AI deployments.
Advanced RAG systems use multi-stage retrieval, vector databases, and reranking for accurate results. This approach handles structured and unstructured data, ensuring reliable retrieval pipelines that satisfy enterprise requirements for accurate knowledge extraction and analytics.
Yes, you can deploy a customer support assistant that answers questions using a live knowledge base. The architecture supports memory and action execution, allowing agents to maintain context and perform tool use across cross-agent collaboration workflows.
Multimodal analytics across structured and unstructured data is fully supported. The system orchestrates LLMs and multimodal capabilities into end-to-end pipelines, enabling enterprise applications to process and analyze diverse data formats within a unified workflow.
LLM integration and model management allows running multiple providers while optimizing costs for production workloads. This involves configuring model selection and routing within the orchestration framework to balance performance and expenditure across enterprise AI deployments.
AradotsoProvision AWS S3 buckets and EC2 instances with Terraform for data engineering.
sugarcrm-developersGenerate custom REST API endpoints for SugarCRM modules by subclassing SugarApi or ModuleApi.
as4584Combine vector and keyword search results using RRF, linear combination, or cross-encoder reranking.
tigredonorteApply small targeted changes on an existing branch without full workflow orchestration.
